Consider someone hunting for an apartment or a house, for a temporary relocation of 12 months. If she wants to use a corporate housing company, then querying “Oakwood corporate housing” or such on a typical search engine (Google™, Yahoo™ Search, MSN™ Search, Ask.com™) might well satisfy her information need. If, however, she wants to rent from other parties, and knows the location in question well enough, searching through Apartments.com's™ catalogue might suffice.
However, if she poses her information need as a query in a “conversational manner”, such as “family of two kids, one dog, looking for an apartment or a house, commuting to West Los Angeles, good elementary schools, one year lease”, then no available online tools can return helpful results to her.
The above example reveals that there is a lack of tools helpful to searchers in the common situation of fulfilling projects such as purchasing, shopping, procurement, bartering and requesting for quotes, in “industries” such as online retail, traditional retail, wholesale, health care, travel, real estate, restaurant-going, entertainment, logistics, and sourcing.
What is needed is a searching experience that is substantially similar to consultation with a human expert. A searcher with a project to accomplish would first find an expert in a given industry sector, and then pose her query. The expert in turn would ask additional questions and solicit response from the searcher. Then the expert typically gives the searcher a list of entities (e.g. providers of products or services) that are helpful in furthering her project.
Important ingredients to delivering such a searching experience include: (1) determining at least one “industry sector” from a user's query; (2) deriving needed additional questions, partially based on the derived industry sector information, and soliciting response from the user; (3) modifying the query into a second query which is formatted so that it facilitates searching (matching and ranking) and displaying search results.
The state of the art contains various elements that could be helpful, however, there is no known solution that contains all of the above ingredients. The state of the art is reviewed below.
(A) Determining an “Industry Sector” From a User Query
The term “industry” and “industry sector” (used exchangeably with “sector”) are used in a broad sense. Within retail, an “industry” could be “electronics”, a sector underneath could be “cameras”, which in turn contains “film cameras” and “digital cameras”.
When a user is settled on a service or product provider, her query might include the name of a service or product provider, as in “CVS in Santa Monica”. Such a query is commonly submitted to “local search” engines, such as local.Google.com. There are more prior art disclosures. For example, U.S. Patent Application, 20070016556 A1, Ann, et al., Jan. 18, 2007, teaches “receiving a query for a destination search, recognizing the industry type, the geographical name”. This and all other extrinsic materials discussed herein are incorporated by reference in their entirety. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provider herein applies and the definition of that term in the reference does not apply.
A user might alternatively be looking for a category of providers. For example, “drugstores in Santa Monica”. This searching need has largely been solved by local search. There are more prior art disclosures. For example, U.S. Pat. No. 6,157,923, Ivler, et al., Dec. 5, 2000, discloses a method of processing a user query that includes “determining a first industry code based on the query”; and “displaying to the user information corresponding to the first industry code, in conjunction with information corresponding to at least one additional industry code which is not a subset or superset of the first industry code”. The “industry code” in question is defined as the SIC codes. For another example, U.S. Patent Application 20060190439 A1, Chowdhury, et al. Aug. 24, 2006, discloses a method for classification of a query that includes “associating the category that is associated with the pattern also with the query phrase or the constituent part”, and “identifying at least one search resource for satisfying the query phrase based on the associated category.”
A user might alternatively form a query to describe a product or service of interest. For example, “SONY DSC-T50”, which is a model name for a digital camera. To recognize such a proper name is also disclosed in prior art. For example, U.S. Patent Application, 20050222977 A1, Zhou, et al., Oct. 6, 2005, teaches “determining whether the entity name is associated with a common word or phrase”, where “an ‘entity,’ as used herein, may refer to anything that can be tagged as being associated with certain documents. Examples of entities may include news sources, stores, such as online stores, product categories, brands or manufacturers, specific product models, condition (e.g., new, used, refurbished, etc.), authors, artists, people, places, and organizations.”
A user query could be assigned a number of “categories” or “topics”. U.S. Pat. No. 7,089,226, Dumais, et al., Aug. 8, 2006, teaches a method that “receives a query and processes probabilities associated with N categories that are collectively assigned a top-level classifier and individually assigned sublevel classifiers, each category having one or more topics, N being an integer”.
However, there is the need for deriving industry sector information from a user query, which cannot be done by just extracting proper names. Such a query might not contain a proper name, or the connection between a proper name to an industry sector is not straightforward. For example, the query “Christmas gifts to 8-11 year olds” can reasonably yield industry sectors such as “toys”, “games”, “clothes”, “books”, defying a simple mapping from the proper name “Christmas” to these industry sectors.
(B) There is the Need for Asking Additional Questions
Often times, after an initial query, additional questions are needed to direct to a searcher. For example, in purchasing, specifications of a product are highly relevant to a purchasing decision, but many times such specifications are not included in the searcher's initial query, for at least two reasons, one, the searcher might not be aware of, or do not know how to ask about, the specifications (e.g., what is the most number of ports a USB port can have); and two, the initial query would be too complicated if it includes many specifications.
Online tools devoted to a particular topic solicit response from searchers. For example, Blue Nile (bluenile.com) has a “Refine Your Search Criteria” feature that solicits feedback from searchers on six specifications: “Shape”, “Carat”, “Cut”, “Color”, “Clarity” and “Price”. By clicking on a box or sliding a scale, a searcher provides feedback which leads to changes in search results. These six specifications are highly relevant to satisfying the searcher's information need, and a human expert would have asked a searcher about them, too.
Many database applications provide users with a list of additional questions, typically in the form of menu choices, once an “industry” is known. For example, U.S. Patent Application, 20020152200 A1, Krichilsky, et al., Oct. 17, 2002, teaches a product searching method that comprises “a step of receiving an industry selection”, “receiving an application selection”, “receiving a filter selection”, and “then receiving a search-property selection”.
There is also prior art in providing “related searches”, such as the “Narrow Your Search” and “Expand Your Search” features by Ask.com™. However, such related searches typically are entirely based on the user query.
What is needed includes (a) dynamically determining additional questions to be asked, partially dependent on both the initial user query and the derived industry sector information; (b) whenever it is possible, the questions are also “adequate”, in that they are sufficient in serving the user's information need. These two needs are many times addressed when a searcher consults with a human expert.
(C) There is the Need for Query Rewriting
It is common that exact matching the entirety or part of a user query yields no search results or few. There is a need, therefore, to rewrite the query so that enough relevant search results could be found. But the state-of-the art addresses generality, and cannot serve the need, some of which are reviewed below.
U.S. Pat. No. 6,006,225, Bowman, et al., Dec. 21, 1999, teaches that “using at least the query term correlation data to identify a plurality of additional query terms that are deemed to be related to the at least one query term”. The method utilizes a “related terms list” mapping a term to a number of other terms, where “each term that appears within the related terms list” is “deemed to be related to the corresponding key term” “by virtue of the relatively high frequency with which the terms have occurred within the same query”.
U.S. Patent Application, 20060206474 A1, Kapur, et al., Sep. 14, 2006, teaches that in matching a query against text of sponsored ads, “modifying the query to produce a modified query using rules designed to increase a chance that the modified query matches more predefined query strings.”
U.S. Patent Application, 20060167842 A1, Watson, Jul. 27, 2006, teaches finding “at least one alternative query if the initial search results are deemed inadequate by the result evaluation mechanism”, and such an alternative query typically is “a sub-query of the original query, with synonyms and thesaurus considered”. The following example is given: for the the input query “blue mini ipod”, two alternative queries are presented to the searcher: “blue ipod” and “Mini Ipod”.
What is needed is powerful query rewriting that goes beyond generality or linguistic transformations, or co-occurrence frequencies.
(D) The Overall Need
Overall, what is needed methods and systems that emulate interactions with an industry expert, so as to offer searching experience that is substantially similar to a searcher's consulting with to an expert in an area of interest, and getting back results that are helpful in the searcher's decision making. Some of the desirable features are listed below.
(I) A human expert would be able to derive industry sector information derivable from a searcher's query. The methods and systems should do so.
(II) A human expert would ask the searcher relevant and adequate additional questions, based both on the derived industry sector information and the user query, and further based on the perspective of the searcher. For example, with the user perspective of purchasing, an expert would guide buyers of goods and services past all the irrelevant information, and focus on the features that distinguish one vendor from the next. Where the buyer is not aware of a particular feature or parameter of interest, the expert would ask relevant questions. The expert would also guide users to consider related products and services that they may have ignored. The methods and systems should do so.
(III) An expert would get feedback from a searcher, re-organize and paraphrase with industry knowledge and jargon so as to be conductive to industry-specific searching for information. Once matching information is found, the expert would give recommendations in rank order. Such recommendations are based upon extensive industry knowledge. In the context of purchasing services or products, the expert would factor in which companies are the most reputable, cost-effective, reliable, and so forth. The methods and systems should do so.
The searching experience typically is multi-cycled, and the query-search and question-response iterations are not unlike a question and answer session a searcher would have with an expert.